Sample Plots Research Articles

Potential of Agroforestry Practices on Woody Species Diversity and Composition: Implication for Conservation of Indigenous Trees in Selected Districts of Tigray, Northern Ethiopia

Human activities such as expanding farmland, producing charcoal, and harvesting construction materials and fuel wood are major contributors to forest degradation and biodiversity loss in Northern Ethiopia. This study aimed to assess woody species' diversity, composition, and threat levels in different agroforestry practices to prioritize their conservation. As the result three potential Agroforestry practices were purposively identified and selected for the study. A total of 45 sample plots, 15 plots from each agroforestry practices such as Woodlot agroforestry (WLAF) (10m x 10m), Homegarden Agroforestry (HGAF) (20m x 10m), and Parkland agroforestry (PLAF) (50m x 100m). Data on woody species were analyzed using Past version 2.17c and SPSS version 20. The results identified 36, 26, and 21 woody plant species from 31, 23, and 19 genera, as well aswell as 22, 16, and 15 families in HGAF, PLAF, and WLAF, respectively. Significant differences (p < 0.001) were found among the agroforestry practices in terms of tree density, species richness, species abundance, and Shannon diversity, though species evenness did not vary. The Importance Value Index (IVI) highlighted the top species in HGAF as A. etbaica (75.2), F. albida (33.1), and A. seyal (30.6). Species with low IVI values, such as B. polystachya (1.56), R. vulgaris (1.57), G. ferruginea (1.62), and C. aurantiifolia (0.90), require significant conservation efforts. In PLAF, the key species were F. albida (131.03), A. seyal (59.33), and C. africana (26.21). In WLAF, E. globulus (61.8), A. seyal (8.83), and C. edulis (31.4) were the most abundant, frequent, and dominant. Trees and shrubs in HGAF and WLAF had smaller stem diameters compared to those in PLAF. However, WLAF had greater tree height and basal area (BA, m²) than both HGAF and PLAF, with HGAF also showing a higher BA than PLAF (p ≤ 0.05). The study concluded that HGAF and PLAF are vital for sustaining local livelihoods, providing food, and conserving biodiversity. These agroforestry systems enhance natural forests and help prevent the extinction of woody species. Therefore, developing and enhancing HGAF, and PLAF in densely populated landscapes should be integral to biodiversity conservation strategies.

Altitudinal Variations of Ground Species in the Southern Aravalli regions of Rajasthan, India

Ground layer species sustain a variety of plants and animals; and maintain a healthy and resilient forest ecosystem by contributing to ecological functioning, structural support, and biodiversity. The western Indian Aravalli range is noted for its vegetation. Studies from these regions indicate that various environmental factors influence plant diversity and its distributions. The present study examines the impacts of altitude on ground species in Rajasthan's southern Aravalli hill ranges. We conducted field investigations year-round in Phulwari Ki Nal Wildlife Sanctuary, Kumbhalgarh Wildlife Sanctuary, and Sitamata Wildlife Sanctuary at different altitudes. A random transect method was used; five 1m2 plots were laid at every 250m interval. Species’ names and numbers were recorded from sampling plots. Sanctuary-wise species richness, density, and diversity were analyzed and related with altitude. The protected areas of Southern Aravalli do not follow an altitude-specific pattern in ground species distribution. Specific lower altitude ranges had the most species richness, density, and diversity. While altitude showed both positive and negative correlations with respect to ground species richness, diversity and density. The study findings help in conserving and preserving ground layer species in the Aravalli regions of Rajasthan.

Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland

Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m2, of which the 22-year average AGB was 48.73 gC/m2 and 22-year average belowground biomass was 988.37 gC/m2, showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p > 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density.

Spatiotemporal coupling dynamics and factors influencing soil organic carbon and crop yield in Chinese farmlands

Clarifying the correlation between soil organic carbon (SOC) and crop yield is key to achieving carbon neutrality and ensuring food security. However, owing to the lack of analysis based on large-scale farmland monitoring data and research on deep soil, relevant research has not yet reached a consensus. Here, we based on the monitoring data of 118 sample plots from 21 typical farmland and farmland compound ecosystem stations of the China Ecosystem Research Network (CERN) between 2004 and 2020, the temporal and spatial coupling associations between SOC content and crop yield in 0–20 cm and 0–100 cm soil layers and its factors influencing were determined. The findings revealed that between 2004 and 2020, SOC content in 0–20 cm soil layer, SOC content in 0–100 cm soil layer, and crop yield in typical farmland in China showed a fluctuating upward trend, the average annual growth rates were 0.59 %, 0.27 % and 1.07 %, respectively. The coupling relationship between SOC content and crop yield was not always good in different periods, which varies largely in different geographical divisions. Among the anthropogenic factors, exogenous carbon input can improve the coupling relationship between them by increasing the soil organic carbon content and crop yield, while the effect of less tillage and no tillage is limited. Among the natural factors, temperature, soil bulk density, and farmland type all have an impact on farmland SOC content and crop yield at different significance levels. Each variable had different effects on SOC content and crop yield in typical farmlands in different geographical regions. With deepening soil layer, influence of anthropogenic factors such as exogenous carbon input on SOC content decreases, but it still cannot be ignored. Based on these findings, the study recommends that exogenous carbon input play an important role in soil carbon sequestration and improving crop yield.

Exploring Floristic Diversity, Propagation Patterns, and Plant Functions in Domestic Gardens across Urban Planning Gradient in Lubumbashi, DR Congo

Urbanization degrades natural habitats and creates new urban ecosystems like domestic gardens. The plant composition of these gardens varies with socio-economic factors and urban planning levels. However, the diversity and impact of introduced species are often poorly assessed, causing potential ecological imbalances (disruptions in the natural functioning and stability of ecosystems), particularly in Lubumbashi (DR Congo). The objective was to analyze the spatial structure, plant diversity, propagation strategies, and ecological functions of domestic gardens. Three distinct neighborhoods were selected: a planned, unplanned, and residential neighborhood. Twenty avenues (with five plots per avenue) were chosen to represent the diversity within each neighborhood, and stratified random sampling of plots was conducted to analyze gardening practices. Gardens were classified into types, and their vegetation was evaluated based on species origin and ecological impact. The analysis of domestic gardens in Lubumbashi reveals significant variations across different neighborhood types. Residential neighborhoods exhibit larger average garden sizes (315.1 m2), higher species richness (22 species), and larger plot sizes (1032 m2) compared to unplanned and planned neighborhoods, where garden areas and species richness are notably lower. Rectangular gardens dominate in unplanned areas, while planned neighborhoods feature more intentional landscaping elements, such as flowerbeds and hedges. The use of gardens for food production is prominent in planned areas (40.7%), whereas residential neighborhoods prioritize ornamentation (51.4%). The study identified 232 taxa across 68 families, with a predominance of exotic species (80%) in all neighborhoods, particularly in unplanned areas (82.25%). The data revealed that Mangifera indica and Persea americana are abundant in all neighborhoods, illustrating their adaptability to different urban contexts. Herbaceous species are most common, followed by woody plants, with vines being sparse. Species dispersal is primarily driven by human activities (anthropochory), accounting for over 85% in all neighborhoods. These findings highlight the strong human influence on the composition and structure of domestic gardens in Lubumbashi, emphasizing the dominance of exotic species and the importance of anthropogenic factors in shaping urban green spaces. Urban policies should incorporate strategies to minimize the negative impacts of exotic species on native flora.

Ecological health evaluation of an urban riverside greenway based on the AHP-EWM-TOPSIS model: a case study of Hangzhou, China

Abstract Riverside greenways are complex artificial–natural composite ecosystems that occupy significant linear open spaces in urban areas. Healthy riverside greenways promote public health and improve urban environmental quality. This study developed an evaluation system with four criteria layers and 31 indicators, using a comprehensive method that combines the analytical hierarchy process (AHP), entropy weight method (EWM), and technique for order preference by similarity to ideal solution (TOPSIS) to assess the ecological health of the Shangtang River greenway in Hangzhou, China. Additionally, structural equation modeling analyzed key factors influencing riverside greenway health and their pathways. Redundancy analysis (RDA) was used to explore how design and non–design factors affect the ecological health of riverside greenways. The results showed health values for different sections of the riverside greenway ranging from 0.16 to 0.69. The use of ecological design techniques was the main factor causing significant variations in health values across sampling plots. Specifically, ecological revetments, the slope of terrain at water entry, and ecological conservation measures positively impacted the ecological health of the riverside greenway, while non–native invasive plants and building density had negative effects. Non–design factors, such as physical states, also play an equally important role in the ecological health of urban riverside greenways. Effectively assessing the ecological health of these greenways is essential for developing management strategies. This study presents a novel framework for evaluating the ecological health of urban riverside greenways by quantifying indicators related to the regional environment, physical conditions, management practices, and design techniques. It quantitatively analyzes how landscape design techniques contribute to ecological health and serves as an empirical tool for improving urban waterfront environments and advancing riverside greenway construction and management.

Characteristics of forest understory herbaceous vegetation and its influencing factors in biodiversity hotspots in China

Forest ecosystems, especially within biodiversity hotspots, heavily rely on the often-overlooked understory herbaceous communities (USHC) for their structural integrity and ecological functions. Because of their modest stature, these communities have garnered insufficient attention from researchers. Therefore, in this study attention is focused on USHC, which attempts to fill this research gap. To achieve this, we established 105 sample plots in 2022 within forest ecosystems situated in the biodiversity hotspot of southwest China. Our investigation unveiled a rich biodiversity tapestry, identifying 424 plant species from 108 families and 284 genera. Notable were the 217 herbaceous species identified in the understory layer, representing 67 families and 163 genera. These understory herbaceous species accounted for 51.2 % of the overall species count. Through a detailed analysis, the sample plots were categorized into eight distinct communities using a two-way indicator species analysis. These communities exhibited diverse variations in species composition, diversity, biomass, and nutrient characteristics. Notably, community I (Eucalyptus robusta Smith + Rubus corchorifolius L. f. + Alternanthera philoxeroides (Mart.) Griseb.), V (Camptotheca acuminata Decne + Rubus parkeri Hance + Iris tectorum Maxim), VIII (Platycladus orientalis (L.) Franco + Viburnum dilatatum Thunb. + Cyclosorus interruptus (Willd.) H. Ito) stood out for their exceptional species diversity and productivity. Moreover, correlation analyses highlighted the substantial impact of topographic and soil factors on USHC. Altitude, soil water content, and total carbon content emerged as the primary determinants of species diversity. Biomass demonstrated close associations with total soil carbon and nitrogen. Furthermore, the nutrient characteristics displayed significant correlations with soil nutrient contents. These findings underscore the critical importance of prioritizing USHC attention in forest conservation and management strategies within biodiversity hotspots. The implications strongly advocate for the inclusion of these communities in planning and decision-making processes, emphasizing their pivotal role in maintaining structural and functional diversity in ecosystems.

Bryophytes in managed lowland forests of Slovakia (Central Europe): looking into species diversity across different forest types

AbstractThe majority of the woodland area in Central Europe is composed of managed forests. Although the species diversity and composition, particularly in older forests, may be similar to those of natural or close-to-nature forests, intensive forestry and other human activities have adverse impacts on biodiversity. The present study focused on bryophyte diversity in managed lowland forests, as these have received less attention compared to other types of forests. Our research targeted the area located within the Borská nížina Lowland in south-western Slovakia. In total, 37 forest sampling plots (SPs) were selected, representing five different forest types, namely Quercus, Fraxinus, Robinia, Pinus, and mixed forests. Multivariate statistical analyses were conducted across these forest types to demonstrate variations in bryophyte species richness, composition, and functional traits. Overall, 60 species of bryophytes were identified, of which five were liverworts and 55 were mosses. Epiphytes were the most abundant substrate group in deciduous and mixed stands, while epigeic species prevailed in Pinus forests. Considering environmental factors, the bryophyte diversity was influenced mostly by the forest type and area size of SPs. Clear differences in species composition were observed when comparing coniferous and deciduous stands, as well as non-native Robinia forests and native tree stands. Overall, intensive forest management results in a high concentration of hemerophilous and nitrophilous species, along with the absence of rare and threatened mosses and liverworts. Nevertheless, by adhering to proper management methods, even managed forests can provide suitable habitats for various bryophytes.

Combining multiple feature selection methods and structural equation modelling for exploring factors affecting stand biomass of natural coniferous-broad leaved mixed forests

Recognition of biotic and abiotic factors affecting biomass of natural mixed forests is of great importance for forest carbon estimation and management. When estimating stand biomass using models, different variable selection methods often yield inconsistent results, and there is lack of systematic analysis. This study aimed to combine multiple feature selection methods with structural equation modelling (SEM) to identify a set of variables affecting stand biomass more reasonably. Eight methods were applied for feature selection based on data from 286 permanent sample plots in natural coniferous-broad leaved mixed forests in northeast China. These methods included Pearson correlation analysis, two methods derived from principal component analysis (PCA), stepwise regression, redundancy analysis (RDA), generalized additive model (GAM), random forest (RF), and boosted regression tree (BRT). A total of 56 candidate variables were considered, covering stand, biodiversity, climate and soil features. Significant variability was observed in the variables selected, however, there were 6 variables consistently identified across all methods, including tree species diversity (N_Sp_Div), stand structural diversity (N_ Size_ Div), nearest taxon index (NRI), community weighted mean based on dry matter mass of leaves (CWM.LDMC), soil pH, and degree-days above 18 °C (DD18). Then, these variables were included in the SEM with stand average age and additive stand density index (aSDI) to explore the direction and magnitude of their impacts on stand biomass. The SEM results showed that aSDI and average age had the greatest positive effects on stand biomass, and structural diversity also had a significant positive effect. DD18 affected stand biomass both directly and indirectly, with the total negative effect. Soil pH indirectly affected stand biomass via aSDI. Our findings demonstrated that combining multiple feature selection methods with SEM was an effective approach for understanding multiple factors affecting stand biomass, and provided valuable insights for forest biomass estimation and carbon management.

The species diversity and phylogenetic structure patterns of desert plant communities in the Turpan-Hami region, Xinjiang

Desert plant communities play a crucial role in enhancing desert ecosystem productivity and stability but are highly susceptible to environmental changes owing to their limited resilience and resistance to disturbances, making it essential to understand the factors influencing their diversity patterns and community structure, particularly under the threat of climate change and intensifying droughts. This study focused on the Turpan-Hami desert region, a typical desert ecosystem in which diversity patterns and community structures remain unclear. We established 101 sampling sites ranging from elevations of -134 to 2056m and recorded community information, including species composition, height, coverage, and density, using the sample plot method. The diversity patterns were analyzed using regression analysis, and the phylogenetic structure was assessed. Additionally, Canonical Correspondence Analysis, Mantel tests, and General Linear Models were used to identify the key factors affecting community structure. Our results demonstrated that the species richness and phylogenetic diversity of plant communities exhibited a monotonic increase with elevation, and the phylogenetic structure exhibited low clustering and high dispersion along the elevational gradient. The annual mean temperature emerged as the most significant factor influencing diversity patterns, with soil nutrients, such as total potassium and available phosphorus, also affecting the spatial distribution of diversity. Notably, no significant correlations were found between community phylogenetic structure and abiotic factors, highlighting the complex interactions that drive diversity in the Turpan-Hami region. This study offers valuable insights into the mechanisms shaping desert plant community diversity and is essential for biodiversity conservation efforts in fragile desert ecosystems amid climate change.

Mapping percent canopy cover using individual tree- and area-based procedures that are based on airborne LiDAR data: Case study from an oak-hickory-pine forest in the USA

Canopy cover (CC) is the proportion of a forest floor covered by the vertical projection of tree crowns. Recently, it has become common to utilize LiDAR (light detection and ranging) canopy metrics to estimate CC over large areas. However, these metrics are primarily related to canopy density rather than the specific definition of CC. Here, two processes that employed individual tree segmentation (ITS) and area-based procedures based on LiDAR data are presented to estimate CC across the Talladega Division of the Talladega National Forest (93,694 ha) at the plot, stand, and landscape levels. The two analytical procedures were assessed using the results of a plot/grid method as a reference dataset, which focused on CC estimates within 255 field measurement fixed-area sample plots. The accuracy of a third process, employing an imagery-based visual CC assessment, was also compared against the two procedures and the reference dataset. The LiDAR-based analytical procedures were able to provide estimates of CC with an RMSE of approximately 15 %, which is acceptable for landscape-level assessments. Based on the results of this study, we conclude that CC maps, when created using LiDAR data, may be suitable for various operational tasks such as assessing the impact of forest disturbances and helping to determine the habitat suitability for certain wildlife species.

Basal Area, Soil, and Elevation Factors Affecting The Essential Oils Yield, Geranyl Acetate, and D-Limonene of Zanthoxylum acanthopodium

Andaliman (Z. acanthopodium) is an aromatic Non-Timber Forest Product (NTFP) plant species that contains essential oils (EOs), which have various benefits for cuisine, medicine, and cosmetics. This study is essential because it is not yet known what biotic factors (basal area) and abiotic factors (soil and elevation) influence the EOs yield, geranyl acetate, and D-limonene as skin anti-aging. This study investigated whether the basal area, soil, and elevation factors influence the EOs yield, geranyl acetate, and D-limonene content of Andaliman fruit. The biotic and abiotic factors were measured from 9 sample plots representing the Andaliman habitats in North Sumatra, Indonesia. A vegetation analysis was conducted to derive the Andaliman basal area of each sample plot. Soil samples were taken randomly in each plot and analyzed for their physicochemical (pH, C, SOM, N, CNR, P, K, Ca, Mg, CEC, BS, SA, DU, CL, WC, and BD) contents. Elevation gradients were measured on each plot. The EOs of Andaliman fruit include yield, geranyl acetate, and D-limonene as skin anti-aging. The data were analyzed using Partial Least Square Regression for Generalized Linear Models (PlsRglm) to determine which biotic and abiotic factors influenced the EOs yield, geranyl acetate, and D-limonene. This study confirmed that clay soil texture was the main factor influencing EOs of Andaliman fruit yield, geranyl acetate, and D-limonene. Apart from soil factors, basal area, and elevation factors were also essential to consider when cultivating Andaliman fruit as a source of skin anti-aging substances. HIGHLIGHTS To the best of the authors’s knowledge, the first study in Indonesia 2024 that integrated biotic and abiotic factors affecting the essential oils (EOs) yield, geranyl acetate and D-limonene contents derived from of acanthopodium fruit as skin anti-aging substance. Using Partial Least Square Regression for Generalized Linear Models (PlsRglm) method for analyzing basal area, soil factors and elevation that influence the EOs of acanthopodium fruit. Of all the abiotic and biotic factors studied, Clay soil texture is the most critical factor that influences acanthopodium fruit EOs as skin anti-aging substances. Basal area and elevation are also essential factors in the cultivation of aromatic Andaliman plants. GRAPHICAL ABSTRACT

Coexistence of territorial competitor ants in fragmented boreal forest landscape

The distribution of species in a patchy habitat may be influenced by competitive interactions. The dominant and highly competitive boreal ant species belong to the Formica rufa group. A pair of species, Formica aquilonia and Formica polyctena, require extensive territories due to their multi-nest breeding habits. The coexistence and habitat patterns of these two wood ant species in the boreal forest landscape were investigated. Forest characteristics in the vicinity of nests in forest patches were similar for both species, but they did not coexist in the same sampling plots of 0.79 ha in forest patches, indicating competitive exclusion. The sampling plots in large forest patches were more occupied by F. aquilonia, while no such association was found for F. polyctena. At a larger spatial scale (78.5 ha), we found that F. polyctena was more tolerant of smaller forest patches than F. aquilonia suggesting that these two ant species can coexist in moderately fragmented forest landscapes. However, forest habitat loss, fragmentation and climate-induced changes in forest tree structure may shift the species balance in favour of F. polyctena over F. aquilonia in the future.

Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors

Abstract Background and aims Invasive species represent a threat to the conservation of biological systems. Riparian ecosystems are vulnerable to plant invasions, as waterflow facilitates the dispersal of plant propagules, while invasive species may subsequently impact soil, including soil microbial communities. Downstream connectivity among disparate riverine segments is expected to cause spatial continuity of abiotic and biotic components of riparian ecosystems. Methods We studied diversity of microbial communities in three headwater streams in Central Europe. Plant diversity, soil properties and soil microbiota were assessed on 20 sample plots per river. Soil microbial activity and community-level physiological profiling were used to study the soil microbial community. Results While the α-diversity of plants and soil microbiota was similar among rivers, plant communities were substantially more differentiated than microbial communities. Richness in alien and invasive plants significantly differed among rivers, which was reflected in different spatial patterns of microbial activity and diversity. A high level of spatial continuity was observed in the Kysuca river with straightened riverbed and artificial surfaces in the adjacent areas. The cover of invasive plants affects the composition of microbial functional groups of riverbed soils. Conclusion The expectation of spatial continuity of riverbed soil properties including those of soil microbiota caused by connectivity between different river segments was only partially fulfilled. Spatial continuity strongly depends on the environmental setting and stream characteristics of a particular river. The presence of invasive herbs affected the functional composition of soil microbiota but had no effect on microbial activity and diversity.

Плотность всходов сосны обыкновенной в сосновых лесах под влиянием выбросов Карабашского медеплавильного комбината и лесных пожаров

We assessed the features of natural pine regeneration in forests with different ages of ground fires in the Southern Urals depending on the level of heavy metal pollution. Using the materials obtained from 81 sample plots laid in maturing, mature and over-mature pine forests of natural origin in three types of forest growth conditions (TFGC 1 – TFGC 3), the relationship between the density of pine seedlings and the state of forest stands (crown density, stand age, vital status index), with the characteristics of vegetation condition (total number of species, total projective cover of herb-shrub layer, projective cover of bryophytes layer), with external influences (pollution level, distance from emission source, duration of fire disturbance), and with the methodological features of the study (accounting year) was analyzed. The high variability of estimates of the density of pine seedlings did not allow us to identify the relationship between the density of seedlings and the level of pollution and/or fire disturbances. The density of seedlings is nonlinearly related to the protective cover of the herb-shrub layer. With increasing coverage of the herb-shrub layer, the density of seedlings increased in areas with high levels of pollution and decreased in areas with background pollution.

A Novel Method for Extracting DBH and Crown Base Height in Forests Using Small Motion Clips

The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing–decreasing-increasing–decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.

Restoration of stand on the site of burnt spruce forest in the Central Forest reserve

With the warming of the climate, the intensification of droughts and the drying up of coniferous forests, the number of forest fires is also increasing. Therefore, the study of the post-fire dynamics of stands and prospects for reforestation on burnt areas is especially relevant. The research was carried out in the Central Forest State Natural Biosphere Reserve (southern taiga) on the burnt area formed in 1999 as a result of a thunderstorm. The purpose of the work was: to describe the state of vegetation 22 years after burnout; to find out the characteristics of the stand formed on the burnt area and the possibility of their use for reconstructing the history of disturbancies in old spruce forests. The objectives of the study included: monitoring vegetation changes in the 1999 burnt area; studying the growth of spruce undergrowth in the burnt area; finding out the number and ratio of preliminary and subsequent renewal of tree species; analyzing the distribution and amount of coals in the sample plots (depending on the intensity of burnout and the state of the pre-fire stand). To study the state of vegetation after the burnout of the blueberry-wood sorrel and wood sorrel – nemoral-herb spruce (Picea abies (L.) H. Karst.) forests, 14 sample plots (100 m2 each) were laid in the northwestern part of the burnt area. The sample plots covered 1.5 hectares. 22 years after the fire, a mixed small-leaved forest with a predominance of birch and a significant participation of spruce has been forming on the site of burnt spruce forests. Spruce is represented mainly by post-fire renewal. In the undergrowth, the ratio of pre- and post-renewal spruce is about 1 : 3. In the ground cover, a small number of nemoral herb species is recorded, and in the stand and undergrowth – almost complete absence of broad-leaved species. A lot of coals were found in soil digs in 4 sample plots (30%); there were no coals in 3 (20%) sample plots, and small coals in small amount were found in 7 (50%) sample plots. Assessing the possibilities of identifying the first post-fire generation of spruce in old stands, it must be said that none of the characteristics of a post-fire spruce stand is universal for its recognition. Only a set of features allows to identify the history of the stand. Distinctive features of the first post-old generation: the presence of coals in 30% of samples or more; a large admixture of birch and aspen; a large diversity of the aspen part of the stand; if there are broad-leaved species, they will be much younger than the spruce; the presence of a sharp decline, and then a rise in growths in a significant part of the oldest spruce trees; the main generation of spruce has initial radial growths on an average of 2.1 mm/year, and the absence of synchronous sharp rises of the growths at the beginning of life. The age of aspen most accurately corresponds to the year of the fire.

Cluster-Based Wood–Leaf Separation Method for Forest Plots Using Terrestrial Laser Scanning Data

Successfully separating wood and leaves in forest plots is a prerequisite for measuring structural parameters and reconstructing 3D forest models. Terrestrial laser scanning (TLS) can distinguish between the leaves and wood of trees through precise and dense point clouds. However, most existing wood–leaf separation methods face significant accuracy issues, especially in dense forests, due to the complications introduced by canopy shading. In this study, we propose a method to separate the wood and leaves in forest plots using the clustering features of TLS data. The method first filters a point cloud to remove the ground points, and then clusters the point cloud using a region-growing algorithm. Next, the clusters are processed based on their sizes and numbers of points for preliminary separation. Chaos Distance is introduced to characterize the observation that wood points are more orderly while leaf points are more chaotic and disorganized. Lastly, the clusters’ Chaos Distance is used for the final separation. Three representative plots were used to validate this method, achieving an average accuracy of 0.938, a precision of 0.927, a recall of 0.892, and an F1 score of 0.907. The three sample plots were processed in 5.18, 3.75, and 14.52 min, demonstrating high efficiency. Comparing the results with the LeWoS and RF models showed that our method better addresses the accuracy issues of complex canopy structures.

Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction.